Abstract

The research presented in this thesis regards the development of a “multi-purpose” microscopic simulation model of railway systems, that can effectively support different design activities, overcoming applicability limits of commercial microscopic models, mostly due to computing inefficiency and the impossibility of being interfaced with automatic structures for system analysis (e.g. “black-box” optimization or uncertainty analysis).
The first part of this work has concerned with the practical development of the model itself: the specification of its modules and respective parameters of input/output; the definition of an open architecture to allow the interfacing with external applications; and the practical implementation in C++ by using an object-oriented programming technique. The simulation process has been then parallelized to let the model be efficient also for large-sized networks or for analyses involving a large number of simulations, especially when launched on multi-core computers. A validation phase has consented to verify the correctness of the implemented code as well as the validity of model outputs by ascertaining their congruence with those observed in a real MRT line, for the same inputs. Then a variance-base sensitivity analysis has been performed to understand how uncertainty in model outputs was apportioned to the different uncertainty sources in model inputs. Such analysis has allowed to identify for a real case-study, the most influent design variables for a certain performance, leading to a more efficient allocation of economic resources available for the intervention.
The second part has regarded several applications of the model to solve different design problems. First, a framework for the optimal design of railway systems has been built-up by integrating the model within a “black-box” optimization loop. The application of this framework to a real case-study consented to identify the equi-block signalling layout that maximized the economic efficiency of investment costs, and the layout which offered the best trade-off between investment costs and user’s satisfaction. In order to effectively support RAM (Reliability, Availabiliy, Maintainability) analysis, an effective modelling architecture has been defined which integrates the microscopic model with an “event-driven” mesoscopic model that considers failure rates of components as input data. Afterwards, the integration with a model for simulating rail passenger flows has led to define a simulation architecture which consents the evaluation of effects induced by a certain intervention, not only on Service Availability (SA) but also on Quality of Service (QoS) offered to customers. Applications results have underlined the usefulness of the developed model and its flexibility in being employed for supporting different decisional tasks relative to both infrastructures and operations of railway systems.